Mutational profiling of mitochondrial DNA reveals an epithelial ovarian cancer-specific evolutionary pattern contributing to high oxidative metabolism

Abstract

Background: Epithelial ovarian cancer (EOC) heavily relies on oxidative phosphorylation (OXPHOS) and exhibits distinct mitochondrial metabolic reprogramming. Up to now, the evolutionary pattern of somatic mitochondrial DNA (mtDNA) mutations in EOC tissues and their potential roles in metabolic remodelling have not been systematically elucidated.

Methods: Based on a large somatic mtDNA mutation dataset from private and public EOC cohorts (239 and 118 patients, respectively), we most comprehensively characterised the EOC-specific evolutionary pattern of mtDNA mutations and investigated its biological implication.

Results: Mutational profiling revealed that the mitochondrial genome of EOC tissues was highly unstable compared with non-cancerous ovary tissues. Furthermore, our data indicated the delayed heteroplasmy accumulation of mtDNA control region (mtCTR) mutations and near-complete absence of mtCTR non-hypervariable segment (non-HVS) mutations in EOC tissues, which is consistent with stringent negative selection against mtCTR mutation. Additionally, we observed a bidirectional and region-specific evolutionary pattern of mtDNA coding region mutations, manifested as significant negative selection against mutations in complex V (ATP6/ATP8) and tRNA loop regions, and potential positive selection on mutations in complex III (MT-CYB). Meanwhile, EOC tissues showed higher mitochondrial biogenesis compared with non-cancerous ovary tissues. Further analysis revealed the significant association between mtDNA mutations and both mitochondrial biogenesis and overall survival of EOC patients.

Conclusions: Our study presents a comprehensive delineation of EOC-specific evolutionary patterns of mtDNA mutations that aligned well with the specific mitochondrial metabolic remodelling, conferring novel insights into the functional roles of mtDNA mutations in EOC tumourigenesis and progression.

Keywords

epithelial ovarian cancer / evolutionary selection / metabolic remodelling / mitochondrial DNA / somatic mutations

References

[1] Sainero-Alcolado L, Liaño-Pons J, Ruiz-Pérez MV, Arsenian-Henriksson M. Targeting mitochondrial metabolism for precision medicine in cancer. Cell Death Differ. 2022;29(7):1304-1317.

[2] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-674.

[3] Yang Y, He J, Zhang B, et al. SLC25A1 promotes tumor growth and survival by reprogramming energy metabolism in colorectal cancer. Cell Death Dis. 2021;12(12):1108.

[4] Wu Y, Zhang X, Wang Z, Zheng W, Cao H, Shen W. Targeting oxidative phosphorylation as an approach for the treatment of ovarian cancer. Front Oncol. 2022;12:971479.

[5] Wang Y, Xie H, Chang X, et al. Single-cell dissection of the multiomic landscape of high-grade serous ovarian cancer. Cancer Res. 2022;82(21):3903-3916.

[6] Saorin A, Di Gregorio E, Miolo G, Steffan A, Corona G. Emerging role of metabolomics in ovarian cancer diagnosis. Metabolites. 2020;10(10):419.

[7] Gentric G, Kieffer Y, Mieulet V, et al. PML-regulated mitochondrial metabolism enhances chemosensitivity in human ovarian cancers. Cell Metab. 2019;29(1):156-173. e10

[8] Tondo-Steele K, McLean K. The “Sweet Spot” of targeting tumor metabolism in ovarian cancers. Cancers (Basel). 2022;14(19):4696.

[9] Li H, Qi Z, Niu Y, et al. FBP1 regulates proliferation, metastasis, and chemoresistance by participating in C-MYC/STAT3 signaling axis in ovarian cancer. Oncogene. 2021;40(40):5938-5949.

[10] Xiao R, You L, Zhang L, et al. Inhibiting the IRE1α axis of the unfolded protein response enhances the antitumor effect of AZD1775 in TP53 mutant ovarian cancer. Adv Sci (Weinh). 2022;9(21):e2105469.

[11] Stewart JB, Chinnery PF. Extreme heterogeneity of human mitochondrial DNA from organelles to populations. Nat Rev Genet. 2021;22(2):106-118.

[12] Smith ALM, Whitehall JC, Greaves LC. Mitochondrial DNA mutations in ageing and cancer. Mol Oncol. 2022;16(18):3276-3294.

[13] Kim M, Mahmood M, Reznik E, Gammage PA. Mitochondrial DNA is a major source of driver mutations in cancer. Trends Cancer. 2022;8(12):1046-1059.

[14] Yuan Y, Ju YS, Kim Y, et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat Genet. 2020;52(3):342-352.

[15] Gammage PA, Frezza C. Mitochondrial DNA: the overlooked oncogenome? BMC Biol. 2019;17(1):53.

[16] Schöpf B, Weissensteiner H, Schäfer G, et al. OXPHOS remodeling in high-grade prostate cancer involves mtDNA mutations and increased succinate oxidation. Nat Commun. 2020;11(1):1487.

[17] Gorelick AN, Kim M, Chatila WK, et al. Respiratory complex and tissue lineage drive recurrent mutations in tumour mtDNA. Nat Metab. 2021;3(4):558-570.

[18] Ju YS, Alexandrov LB, Gerstung M, et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer. Elife. 2014;3:e02935.

[19] Ji X, Guo W, Gu X, et al. Mutational profiling of mtDNA control region reveals tumor-specific evolutionary selection involved in mitochondrial dysfunction. EBioMedicine. 2022;80:104058.

[20] Matulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J, Karlan BY. Ovarian cancer. Nat Rev Dis Primers. 2016;2:16061.

[21] Wang Y, Liu VW, Xue WC, Cheung AN, Ngan HY. Association of decreased mitochondrial DNA content with ovarian cancer progression. Br J Cancer. 2006;95(8):1087-1091.

[22] Guo W, Liu Y, Ji X, et al. Mutational signature of mtDNA confers mechanistic insight into oxidative metabolism remodeling in colorectal cancer. Theranostics. 2023;13(1):324-338.

[23] Xu Z, Zhou K, Wang Z, et al. Metastatic pattern of ovarian cancer delineated by tracing the evolution of mitochondrial DNA mutations. Exp Mol Med. 2023;55:1388-1398.

[24] Li X, Guo X, Li D, et al. Multi-regional sequencing reveals intratumor heterogeneity and positive selection of somatic mtDNA mutations in hepatocellular carcinoma and colorectal cancer. Int J Cancer. 2018;143(5):1143-1152.

[25] Guo S, Zhou K, Yuan Q, et al. An innovative data analysis strategy for accurate next-generation sequencing detection of tumor mitochondrial DNA mutations. Mol Ther Nucleic Acids. 2021;23:232-243.

[26] Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i890.

[27] Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754-1760.

[28] Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079.

[29] Chen L, Liu P, Evans TC, Ettwiller LM. DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science. 2017;355(6326):752-756.

[30] Diossy M, Sztupinszki Z, Krzystanek M, et al. Strand orientation bias detector to determine the probability of FFPE sequencing artifacts. Brief Bioinform. 2021;22(6):bbab186.

[31] Su L, Guo S, Guo W, et al. mitoDataclean: a machine learning approach for the accurate identification of cross-contamination-derived tumor mitochondrial DNA mutations. Int J Cancer. 2022;150(10):1677-1689.

[32] Zhou K, Mo Q, Guo S, et al. A novel next-generation sequencing-based approach for concurrent detection of mitochondrial DNA copy number and mutation. J Mol Diagn. 2020;22(12):1408-1418.

[33] Guo W, Liu Y, Su L, et al. mitoSomatic: a tool for accurate identification of mitochondrial DNA somatic mutations without paired controls. Mol Oncol. 2022;17:857-871.

[34] Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.

[35] Bianco SD, Parca L, Petrizzelli F, et al. APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants. Nat Commun. 2023;14(1):5058.

[36] Sonney S, Leipzig J, Lott MT, et al. Predicting the pathogenicity of novel variants in mitochondrial tRNA with MitoTIP. PLoS Comput Biol. 2017;13(12):e1005867.

[37] Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-w102.

[38] Huang Q, Zhan L, Cao H, et al. Increased mitochondrial fission promotes autophagy and hepatocellular carcinoma cell survival through the ROS-modulated coordinated regulation of the NFKB and TP53 pathways. Autophagy. 2016;12(6):999-1014.

[39] Chandrashekar DS, Karthikeyan SK, Korla PK, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27.

[40] Wittenhagen LM, Kelley SO. Impact of disease-related mitochondrial mutations on tRNA structure and function. Trends Biochem Sci. 2003;28(11):605-611.

[41] Yin C, Li DY, Guo X, et al. NGS-based profiling reveals a critical contributing role of somatic D-loop mtDNA mutations in HBV-related hepatocarcinogenesis. Ann Oncol. 2019;30(6):953-962.

[42] Sanchez-Contreras M, Sweetwyne MT, Tsantilas KA, et al. The multi-tissue landscape of somatic mtDNA mutations indicates tissue-specific accumulation and removal in aging. Elife. 2023;12:e83395.

[43] Stoneking M. Hypervariable sites in the mtDNA control region are mutational hotspots. Am J Hum Genet. 2000;67(4):1029-1032.

[44] Sriramkumar S, Sood R, Huntington TD, et al. Platinum-induced mitochondrial OXPHOS contributes to cancer stem cell enrichment in ovarian cancer. J Transl Med. 2022;20(1):246.

[45] Shen L, Sun B, Sheng J, et al. PGC1α promotes cisplatin resistance in human ovarian carcinoma cells through upregulation of mitochondrial biogenesis. Int J Oncol. 2018;53(1):404-416.

[46] Nayak AP, Kapur A, Barroilhet L, Patankar MS. Oxidative phosphorylation: a target for novel therapeutic strategies against ovarian cancer. Cancers (Basel). 2018;10(9):337.

[47] Matassa DS, Amoroso MR, Lu H, et al. Oxidative metabolism drives inflammation-induced platinum resistance in human ovarian cancer. Cell Death Differ. 2016;23(9):1542-1554.

[48] Koc ZC, Sollars VE, Bou Zgheib N, Rankin GO, Koc EC. Evaluation of mitochondrial biogenesis and ROS generation in high-grade serous ovarian cancer. Front Oncol. 2023;13:1129352.

[49] Filograna R, Koolmeister C, Upadhyay M, et al. Modulation of mtDNA copy number ameliorates the pathological consequences of a heteroplasmic mtDNA mutation in the mouse. Sci Adv. 2019;5(4):eaav9824.

[50] Jiang M, Kauppila TES, Motori E, et al. Increased total mtDNA copy number cures male infertility despite unaltered mtDNA mutation load. Cell Metab. 2017;26(2):429-436. e4

[51] Hahn A, Zuryn S. Mitochondrial genome (mtDNA) mutations that generate reactive oxygen species. Antioxidants (Basel). 2019;8(9):392.

[52] Klimova T, Chandel NS. Mitochondrial complex III regulates hypoxic activation of HIF. Cell Death Differ. 2008;15(4):660-666.

[53] Grandhi S, Bosworth C, Maddox W, et al. Heteroplasmic shifts in tumor mitochondrial genomes reveal tissue-specific signals of relaxed and positive selection. Hum Mol Genet. 2017;26(15):2912-2922.

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2024 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.